{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import math\n",
    "import random\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ELM_Algo:\n",
    "    \n",
    "    def __init__(self,X,Y,X_test,Y_test):\n",
    "        self.X = X\n",
    "        self.Y = Y\n",
    "        self.X_test = X_test\n",
    "        self.Y_test = Y_test\n",
    "    \n",
    "    def sigmoid(self,Z):\n",
    "        A = 1/(1+np.exp(-Z))\n",
    "        return A\n",
    "    \n",
    "    def ELM(self,wgt,N,ftr):\n",
    "        self.W = wgt[:N*ftr,0].reshape(N,ftr)\n",
    "        self.b = wgt[N*ftr:,0].reshape(N,1)\n",
    "        Z = np.dot(self.W,self.X)+self.b\n",
    "        H = self.sigmoid(Z)\n",
    "        self.beta = np.dot(self.Y,np.linalg.pinv(H))\n",
    "    \n",
    "    def predict(self):\n",
    "        Z = np.dot(self.W,self.X_test)+self.b\n",
    "        H = self.sigmoid(Z)\n",
    "        self.Y_pred = np.dot(self.beta,H)\n",
    "    \n",
    "    def rmse(self):\n",
    "        error = np.sum((self.Y_test-self.Y_pred)**2)\n",
    "        error = error/(self.Y_test.shape[0]*self.Y_test.shape[1])\n",
    "        error = math.sqrt(error)\n",
    "        nrm = np.linalg.norm(self.beta)\n",
    "        return (error,nrm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DE:\n",
    "    \n",
    "    def __init__(self,node,elm,I):\n",
    "        self.F = 1\n",
    "        self.elm = elm\n",
    "        self.max_gen = 100\n",
    "        self.N = node\n",
    "        self.ftr = I\n",
    "        self.D = self.N*self.ftr + self.N\n",
    "        self.NP = 6*self.D\n",
    "        self.CR = 0.8\n",
    "    \n",
    "    def generate_pop(self):\n",
    "        self.population = np.random.rand(self.D,self.NP)\n",
    "            \n",
    "    def cost(self,weights):\n",
    "        \n",
    "        error = []\n",
    "        for i in range(weights.shape[1]):\n",
    "            self.elm.ELM(weights[:,[i]],self.N,self.ftr)\n",
    "            self.elm.predict()\n",
    "            error.append(self.elm.rmse())\n",
    "        \n",
    "        return error\n",
    "    \n",
    "    def optimize(self):\n",
    "        self.generate_pop()\n",
    "        score = self.cost(self.population)\n",
    "        ct = 1\n",
    "        while(ct<self.max_gen):\n",
    "            for i in range(self.NP):\n",
    "                idx = random.sample([vl for vl in range(self.D) if vl!=i],3)\n",
    "                j = random.randint(0,self.D-1)\n",
    "                candidate = np.zeros(shape=(self.D,1))\n",
    "                for k in range(self.D):\n",
    "                    if random.random()<self.CR or k==self.D-1:\n",
    "                        candidate[j,0] = self.population[j,idx[0]] + self.F*(self.population[j,idx[1]]-self.population[j,idx[2]])\n",
    "                    else:\n",
    "                        candidate[j,0]=self.population[j,i]\n",
    "                    j = (j+1)%self.D\n",
    "                \n",
    "                \n",
    "                cdt_score = self.cost(candidate)\n",
    "                print(cdt_score[0])\n",
    "                print(score[i])\n",
    "                if score[i][0]-cdt_score[0][0]>0.9*score[i][0]:\n",
    "                    self.population[:,i]=candidate[:,0]\n",
    "                    score[i]=cdt_score[0]\n",
    "                elif abs(score[i][0]-cdt_score[0][0])<0.9*score[i][0] and score[i][1]>cdt_score[0][1]:\n",
    "                    self.population[:,i]=candidate[:,0]\n",
    "                    score[i]=cdt_score[0]\n",
    "            ct+=1\n",
    "        \n",
    "        return (self.population,score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "<p>15000 rows × 11 columns</p>\n",
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      ],
      "text/plain": [
       "                  0             1         2         3             4  \\\n",
       "0      4.678919e-12  3.480796e-03  0.339943  0.999947  1.025697e-02   \n",
       "1      3.451411e-02  4.141693e-02  0.334194  0.987191  1.225635e-01   \n",
       "2     -3.478890e-03  1.391556e-02  0.339566  0.999106  4.101645e-02   \n",
       "3      3.478807e-03 -1.391523e-02  0.339558  0.999106 -4.101645e-02   \n",
       "4     -2.349911e-11  1.738930e-02  0.339370  0.998685  5.126342e-02   \n",
       "...             ...           ...       ...       ...           ...   \n",
       "14995 -1.391361e-02  6.956804e-03  0.339483  0.998948  2.050716e-02   \n",
       "14996  1.391154e-02 -1.043365e-02  0.339373  0.998685 -3.075805e-02   \n",
       "14997 -2.302126e-16  8.518629e-17  0.339963  1.000000  1.637032e-15   \n",
       "14998 -1.042648e-02 -2.085297e-02  0.338901  0.997634 -6.149468e-02   \n",
       "14999 -1.038734e-02 -4.154936e-02  0.336346  0.991962 -1.227581e-01   \n",
       "\n",
       "                  5             6     7     8     9    10  \n",
       "0      5.312083e-12  2.533179e-09  18.0  11.0  18.0  10.0  \n",
       "1     -1.021362e-01 -2.528841e-09  19.0  20.0   9.0   8.0  \n",
       "2      1.025411e-02 -2.534809e-09   9.0  18.0  10.0  14.0  \n",
       "3     -1.025411e-02 -2.531616e-09  19.0  12.0  18.0  16.0  \n",
       "4     -2.759010e-11 -2.532731e-09  16.0  17.0  16.0  12.0  \n",
       "...             ...           ...   ...   ...   ...   ...  \n",
       "14995  4.101431e-02 -2.532267e-09  16.0  19.0  20.0  17.0  \n",
       "14996 -4.101074e-02 -2.534900e-09  15.0  14.0  11.0  17.0  \n",
       "14997  2.141618e-15 -2.533197e-09  16.0  16.0  16.0  16.0  \n",
       "14998  3.074734e-02 -2.534323e-09  12.0   9.0  15.0  15.0  \n",
       "14999  3.068953e-02 -2.519052e-09   8.0   8.0  11.0  20.0  \n",
       "\n",
       "[15000 rows x 11 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"Dataset/Static_Model/15000DwithQuat.csv\")\n",
    "dataS = data.drop('Unnamed: 0',axis=1)\n",
    "dataS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler(feature_range=(0,1))\n",
    "scaler.fit(dataS)\n",
    "dataS = scaler.transform(dataS)\n",
    "X = dataS[:,:7]\n",
    "Y = dataS[:,7:]\n",
    "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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    {
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    {
     "name": "stdout",
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    {
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    {
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    {
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    {
     "name": "stdout",
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    {
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     "output_type": "stream",
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      "(0.20458685107817848, 853.0501355679895)\n",
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      "(0.17876844879597575, 3228.839313310424)\n",
      "(0.1563232114213506, 5000.053348258535)\n",
      "(0.15945367877232067, 9136.438147022016)"
     ]
    }
   ],
   "source": [
    "elm = ELM_Algo(X_train.T,Y_train.T,X_test.T,Y_test.T)\n",
    "de = DE(50,elm,X.shape[1])\n",
    "val = de.optimize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.221331369010963, 10.848866235430291),\n",
       " (0.2213338198953411, 12.214999504858953),\n",
       " (0.22149432538261743, 13.78089779692806),\n",
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     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "val[1]"
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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